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1. Interneuron Desynchronization Precedes Seizures in a Mouse Model of Dravet Syndrome

   https://doi.org/10.1523/JNEUROSCI.2370-19.2020  

   Tran, Conny H.; Vaiana, Michael; Nakuci, Johan; Somarowthu, Ala; Goff, Kevin M.; Goldstein, Nitsan; Murthy, Priya; Muldoon, Sarah F.; Goldberg, Ethan M. (March 2020, The Journal of Neuroscience)
2. Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures

   https://doi.org/10.1038/s41598-020-65401-6  

   Bomela, Walter; Wang, Shuo; Chou, Chun-An; Li, Jr-Shin (December 2020, Scientific Reports)
3. Accuracy of Automated Machine Learning Software in Identifying EEGs with Prolonged Seizures

   Lin, Rebecca; Marquez, Destiny; Jacobson, Mercedes; Castaldi, Hannah; Buckman, Samuel; Shah, Vinit; Picone, Joseph (April 2020, Annual Meeting of the American Academy of Neurology (AAN))

   null (Ed.)

   Objective: To demonstrate that combining automatic processing of EEG data using high performance machine learning algorithms with manual review by expert annotators can quickly identify subjects with prolonged seizures. Background: Prolonged seizures are markers of seizure severity, risk of transformation into status epilepticus, and medical morbidity. Early recognition of prolonged seizures permits intervention and reduces morbidity. Design/Methods: We triaged the TUH EEG Corpus, an open source database of EEGs, by running a state-of-the-art hybrid LSTM-based deep learning system. Then, we postprocessed the output to identify high confidence hypotheses for seizures that were greater than three minutes in duration. Results: The triaging method selected 25 subjects for further review. 17 subjects had seizures; only 5 met criteria for seizures greater than 3 minutes. 11 subjects did not have a prior diagnosis of epilepsy. Among these, 63% had acute respiratory failure and 36% had cardiac arrest leading to seizures secondary to anoxic brain injury. 18 (72%) EEGs were obtained in long-term monitoring (LTM), 1 (4%) in the epilepsy monitoring unit (EMU), and 6 (24%) as a routine EEG (rEEG). 72.2% of seizures in LTM were identified correctly versus 66.7% in rEEGs. Of the 9 subjects who were deceased, 7 (78%) had been on LTM. The seizure detection algorithm misidentified seizures in 7 subjects (28%). A total of 22 (88%) subjects had some ictal pattern. Patterns mistaken for seizure activity included muscle artifact, generalized periodic discharges, generalized spike-and-wave, triphasic waves, and interestingly, an EEG recording captured during CPR. Conclusions: This hybrid approach, which combines state-of-the-art machine learning seizure detection software with human annotation, successfully identified prolonged seizures in 72% of subjects; 88% had ictal patterns. Prolonged seizures were more common in LTM subjects than the EMU and were associated with acute cardiac or pulmonary insult. 

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4. Multimodal, Multiscale Insights into Hippocampal Seizures Enabled by Transparent, Graphene-Based Microelectrode Arrays

   https://doi.org/10.1523/eneuro.0386-21.2022  

   Mulcahey, Patrick J.; Chen, Yuzhang; Driscoll, Nicolette; Murphy, Brendan B.; Dickens, Olivia O.; Johnson, A. T.; Vitale, Flavia; Takano, Hajime (May 2022, eneuro)

   Hippocampal seizures are a defining feature of mesial temporal lobe epilepsy (MTLE). Area CA1 of the hippocampus is commonly implicated in the generation of seizures, which may occur because of the activity of endogenous cell populations or of inputs from other regions within the hippocampal formation. Simultaneously observing activity at the cellular and network scales in vivo remains challenging. Here, we present a novel technology for simultaneous electrophysiology and multicellular calcium imaging of CA1 pyramidal cells (PCs) in mice enabled by a transparent graphene-based microelectrode array (Gr MEA). We examine PC firing at seizure onset, oscillatory coupling, and the dynamics of the seizure traveling wave as seizures evolve. Finally, we couple features derived from both modalities to predict the speed of the traveling wave using bootstrap aggregated regression trees. Analysis of the most important features in the regression trees suggests a transition among states in the evolution of hippocampal seizures. 

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5. Investigation of cannabidiol’s potential targets in limbic seizures. In-silico approach

   https://doi.org/10.1080/07391102.2022.2124454  

   Olafuyi, Olabimpe; Kapusta, Karina; Reed, Alexander; Kolodziejczyk, Wojciech; Saloni, Julia; Hill, Glake A. (September 2022, Journal of Biomolecular Structure and Dynamics)